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lifeart/danetki-qwen3-0.6b
danetki-qwen3-0.6b is a text generation model from lifeart. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
A Qwen3-0.6B model fine-tuned with QLoRA for playing Данетки — a Russian lateral thinking puzzle game. The host describes a mysterious situation and players ask yes/no questions to figure out what happened. This model…
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From the Hugging Face model README
A Qwen3-0.6B model fine-tuned with QLoRA for playing Данетки — a Russian lateral thinking puzzle game. The host describes a mysterious situation and players ask yes/no questions to figure out what happened. This model acts as the host, answering player questions.
Try it in your browser — runs entirely client-side via WebGPU, no server required.
Given a puzzle context (condition + answer) and a player's question, respond with exactly one word:
| Response | Meaning |
|---|---|
| Да | Yes |
| Нет | No |
| Не важно | Irrelevant |
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "lifeart/danetki-qwen3-0.6b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
# Override chat template to standard chatml (no <think> tags)
CHAT_TEMPLATE = (
"{%- for message in messages %}"
"{{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>\\n' }}"
"{%- endfor %}"
"{%- if add_generation_prompt %}"
"{{- '<|im_start|>assistant\\n' }}"
"{%- endif %}"
)
tokenizer.chat_template = CHAT_TEMPLATE
messages = [
{
"role": "system",
"content": "Ты ведущий игры Данетки. Тебе дан контекст и вопрос игрока. Отвечай ТОЛЬКО одним словом: Да, Нет, или Не важно."
},
{
"role": "user",
"content": (
"Контекст: Человек зашёл в бар и попросил стакан воды. "
"Бармен достал пистолет и направил на него. "
"Человек поблагодарил и ушёл.\n\n"
"Разгадка: У человека была икота, и бармен испугал его, "
"чтобы она прошла.\n\n"
"Вопрос: Человек хотел пить воду?"
)
}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=10, temperature=0.1, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response) # "Нет"
Important: Override
tokenizer.chat_templatewith standard chatml (as shown above). The model was trained with this template — Qwen3's default template includes<think>tags which are incompatible.
Ты ведущий игры Данетки. Тебе дан контекст и вопрос игрока. Отвечай ТОЛЬКО одним словом: Да, Нет, или Не важно.
Контекст: {condition}
Разгадка: {answer}
Вопрос: {question}
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-0.6B |
| Fine-tuning method | QLoRA (4-bit quantized base + LoRA adapters, merged) |
| LoRA rank (r) | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.15 |
| LoRA targets | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Language | Russian |
| License | Apache 2.0 |
| Task | Classification — Да / Нет / Не важно |
~183K examples (train) / ~28K (val) from:
| Parameter | Value |
|---|---|
| Learning rate | 2e-5 |
| LR schedule | Cosine with 10% warmup |
| Weight decay | 0.05 |
| Label smoothing | 0.05 |
| Precision | bf16 |
| Batch size | 8 (per device) x 2 (gradient accumulation) = 16 effective |
| Max sequence length | 512 |
| Loss | Completion-only with prompt/completion format |
| Chat template | Standard chatml (no <think> tags) |
| Early stopping | Patience 5, best checkpoint by eval_loss |
| Hardware | NVIDIA L40S (HF Jobs) |
Evaluated on 190 test questions across 25 puzzles (greedy decoding):
| Configuration | Correct | Accuracy | Macro F1 |
|---|---|---|---|
| Baseline (no facts) | 138/190 | 73% | 71.7% |
| With facts injection | 144/190 | 76% | 74.6% |
| Delta | +6 | +3pp | +2.9pp |
| Class | Precision | Recall | F1 |
|---|---|---|---|
| Да | 71.8% | 91.4% | 80.4% |
| Нет | 77.8% | 59.3% | 67.3% |
| Не важно | 83.3% | 70.0% | 76.1% |
@misc{danetki-qwen3-0.6b,
title={Danetki Qwen3-0.6B: Fine-tuned for Russian Lateral Thinking Puzzles},
author={lifeart},
year={2025},
url={https://huggingface.co/lifeart/danetki-qwen3-0.6b}
}